Power control and capacity analysis for a packetized indoor multimedia DS-CDMA network
Bibliographic record
Abstract
This paper proposes a packetized indoor wireless system using direct-sequence code-division multiple-access (DS-CDMA) protocol. The indoor radio environment is characterized by slow Rayleigh fading with or without lognormal shadowing. The system supports multimedia services with various transmission rates and quality of service (QoS) requirements and allows for seamless interfacing to asynchronous transfer mode (ATM) broadband networks. All packets are transmitted with forward error correction (FEC) using convolutional code for voice packets and Bose-Chaudhuri-Hocquenghem (BCH) code for data packets with an automatic retransmission request (ARQ) protocol and for video packets without ARQ. A queueing model is used for servicing data transmission requests. A power control algorithm is proposed for the system, which combines closed-loop power control with channel estimation to give the best performance. The cell capacity of each traffic type and various multimedia traffic configurations in both single-cell and multiple-cell networks are evaluated theoretically under the assumption of perfect power control. The effect of power control imperfection on the capacity using the proposed power control algorithm is investigated by computer simulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".